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Found 6 papers, 2 papers with code
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation
Federated learning (FL) provides a promising collaborative framework to build a model from distributed clients, and this work investigates the carbon emission of the FL process.
Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks
Direct sharing of the data distribution may be prohibitive due to the additional private information that is sent from the clients.
FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks
One major challenge preventing the wide adoption of FL in IoT is the pervasive power supply constraints of IoT devices due to the intensive energy consumption of battery-powered clients for local training and model updates.
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection
Developing a mechanism for battling financial crimes is an impending task that requires in-depth collaboration from multiple institutions, and yet such collaboration imposed significant technical challenges due to the privacy and security requirements of distributed financial data.
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge.
MDA: Availability-Aware Federated Learning Client Selection
Since many devices may be unavailable in cross-device FL, and communication between the server and all clients is extremely costly, only a fraction of clients gets selected for training at each round.
